An Efficient Task Scheduling for Latency Sensitive Tasks in Edge - Cloud Computing Environment

With an emphasis on fulfilling deadlines, this study presents an efficient method for workload scheduling in edge-cloud collaborative computing settings. It optimizes task distribution taking into account variables like task arrival rate and resource availability. A genetic algorithm-based approach is suggested to improve the procedure of scheduling further. It makes use of task prioritization, mutation, and solution encoding. To achieve deadline fulfillment and resource optimization in edge-cloud collaborative computing systems, this method successfully tackles the problems of fluctuating user demands and resource constraints. It also provides an effective solution for dynamic task scheduling.